Portfolio Performance Analysis Report
Judy Northrop
2025-06-22
Executive Summary
This report analyzes the performance of our optimized portfolio strategy across different market regimes and compares it against relevant benchmarks.
Portfolio Optimization Methodology
Our portfolio optimization strategy leverages advanced quantitative methods to construct liquid S&P 500 sector-diverse portfolios using principles of Markowitz Modern Portfolio Theory (MPT). The optimization framework combines cutting-edge statistical techniques with robust numerical optimization to deliver superior risk-adjusted returns.
Key Components:
1. Covariance Estimation with Ledoit-Wolf Shrinkage - We employ the Ledoit-Wolf shrinkage estimator to obtain more reliable covariance matrix estimates - This approach addresses the notorious instability of sample covariance matrices, particularly in high-dimensional settings - The shrinkage technique combines the sample covariance with a structured target matrix, reducing estimation error and improving out-of-sample performance
2. Sharpe Ratio Maximization with DEoptim - Portfolio weights are optimized using Differential Evolution (DEoptim), a robust global optimization algorithm - Our objective function maximizes the Sharpe ratio using a 4% annual risk-free rate assumption - This approach optimizes the risk-adjusted return by maximizing excess return per unit of volatility
3. Sector Diversification and Liquidity Constraints - Stock selection focuses on liquid S&P 500 constituents across all major sectors - Position sizing constraints (2-10% per holding) ensure proper diversification and risk management - Regular rebalancing (30-day frequency) maintains optimal allocation while controlling transaction costs
This methodology produces portfolios that are theoretically grounded in modern portfolio theory while being practically implementable with real-world constraints and market frictions.
Overall Performance
Overall Strategy Performance
Performance by Market Regime
Bull Market Period (2018-2019)
## Loading bull returns from: daily_returns_actual_bull_20250622_161545.rds
COVID Period (2020-2021)
## Loading covid returns from: daily_returns_actual_covid_20250622_161548.rds
Recovery Period (2022-2025)
## Loading recovery returns from: daily_returns_actual_recovery_20250622_161551.rds
Detailed Performance Metrics
| Metric | Optimized 30-day | Equal Weight | SPY | |
|---|---|---|---|---|
| Annualized Return | Annualized Return | 0.1681 | 0.1225 | 0.1076 |
| Annualized Std Dev | Annualized Std Dev | 0.2062 | 0.2188 | 0.1998 |
| Annualized Sharpe (Rf=0%) | Annualized Sharpe (Rf=0%) | 0.8152 | 0.5598 | 0.5386 |
| Semi Deviation | Semi Deviation | 0.0096 | 0.0103 | 0.0093 |
| Gain Deviation | Gain Deviation | 0.0088 | 0.0093 | 0.0086 |
| Loss Deviation | Loss Deviation | 0.0105 | 0.0116 | 0.0105 |
| Downside Deviation (MAR=210%) | Downside Deviation (MAR=210%) | 0.0139 | 0.0145 | 0.0138 |
| Downside Deviation (Rf=0%) | Downside Deviation (Rf=0%) | 0.0092 | 0.0100 | 0.0091 |
| Downside Deviation (0%) | Downside Deviation (0%) | 0.0092 | 0.0100 | 0.0091 |
| Maximum Drawdown | Maximum Drawdown | 0.2782 | 0.3941 | 0.3573 |
| Historical VaR (95%) | Historical VaR (95%) | -0.0195 | -0.0204 | -0.0187 |
| Historical ES (95%) | Historical ES (95%) | -0.0308 | -0.0339 | -0.0311 |
| Modified VaR (95%) | Modified VaR (95%) | -0.0194 | -0.0206 | -0.0188 |
| Modified ES (95%) | Modified ES (95%) | -0.0385 | -0.0413 | -0.0364 |
| Worst Drawdown | Worst Drawdown | 0.2782 | 0.3941 | 0.3573 |
Current Portfolio Initialization
| Ticker | Company Name | Sector | Weight (%) |
|---|---|---|---|
| NVDA | NVIDIA Corporation | Technology | 10.0 |
| AAPL | Apple Inc. | Technology | 10.0 |
| MSFT | Microsoft Corporation | Technology | 9.5 |
| GOOGL | Alphabet Inc. | Technology | 8.5 |
| JPM | JPMorgan Chase & Co. | Financials | 8.0 |
| XOM | Exxon Mobil Corporation | Energy | 7.5 |
| UNH | UnitedHealth Group | Health Care | 7.0 |
| NEE | NextEra Energy | Utilities | 6.5 |
| AMT | American Tower Corp | Real Estate | 6.0 |
| PLD | Prologis Inc | Real Estate | 5.5 |
| CAT | Caterpillar Inc. | Industrials | 5.0 |
| JNJ | Johnson & Johnson | Health Care | 4.5 |
| WMT | Walmart Inc. | Consumer Discretionary | 4.0 |
| HD | Home Depot Inc. | Consumer Discretionary | 3.5 |
| PG | Procter & Gamble Co. | Consumer Discretionary | 3.0 |
| KO | Coca-Cola Company | Consumer Discretionary | 2.5 |
| TSLA | Tesla Inc. | Consumer Discretionary | 2.0 |
| VZ | Verizon Communications | Utilities | 2.0 |
Conclusions
Key Findings:
1. Risk-Return Trade-off Pattern - In bull markets (2018-2019): The optimized portfolio initially underperforms SPY and equal-weight strategies - During market stress (COVID 2020): The optimized portfolio demonstrates superior downside protection - In recovery periods: The strategy captures upside while maintaining risk controls
2. This Performance Pattern Reflects Design Intent - The strategy maximizes Sharpe ratio (risk-adjusted returns) rather than absolute returns - Conservative positioning during bull markets provides “insurance” for inevitable downturns - Over full market cycles, the strategy delivers superior risk-adjusted performance
3. Practical Implications - During strong bull markets, expect some underperformance vs. market indices - The strategy’s value becomes most apparent during market corrections - Long-term investors benefit from lower drawdowns and smoother equity curves
Implementation:
Portfolio Initialization Timing - Consider market regime when initializing the portfolio - In strong bull markets, consider: - Gradual position building over 2-3 months - Higher initial allocation to growth sectors - Or accepting short-term underperformance for long-term protection
Ongoing Management - Initialize portfolio using the weights shown in the Current Portfolio section above - Rebalance every 30 days to maintain optimal allocation - Monitor regime changes for potential strategy adjustments - Consider transaction costs when implementing
Implementation Notes:
- The current portfolio implementation uses the most recent 200 days of data
- Stocks are selected from major sectors to ensure diversification
- Minimum weight of 2% and maximum of 10% per position for risk management
- Expected metrics are based on historical optimization and may vary
To generate a live portfolio recommendation with current market data:
Rscript scripts/examples/current_portfolio_recommendation.RReport generated on 2025-06-22 using ggplot2 performance visualization
Disclaimer: This report is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always consult a licensed financial advisor before making investment decisions.